Research on Tool Remaining Life Prediction Method Based on CNN-LSTM-PSO

Wang, Shuo; Yu, Zhenliang; Xu, Ge; Zhang, Feng

doi:10.1109/access.2023.3299849

Cited by 9 publications

(1 citation statement)

References 33 publications

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“…The method showed good performance in terms of prediction accuracy and reliability. Wang et al [29] proposed a CNN-LSTM-PSO tool residual life prediction method based on multi-channel feature fusion to address the problems of weak tool wear state features, difficult extraction, and low prediction precision and accuracy. Cao et al [30] used a parallel GRU, a comprehensive strategy of a two-stage attention mechanism and nonparametric uncertainty quantification methods to obtain accurate and reliable prediction results.…”

Section: Introductionmentioning

confidence: 99%

Remaining Life Prediction of Bearings Based on Improved IF-SCINet

Zhang,

et al. 2024

IEEE Access

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In the field of health management, predicting the remaining useful life (RUL) of a device becomes critical. However, the RUL prediction process is often affected by a various of confounding factors, resulting in reduced prediction accuracy. To improve the accuracy of RUL prediction, this study first extracts the root mean square, skewness, and Kurtosis from the bearing characteristics, and adopts the multidimensional scale change features to construct a health indicator that fully reflects the bearing degradation trend. Then, a combination of the Isolation forest algorithm and the 3 criterion was used to adaptively determine the first prediction time (FPT) of the bearings. Subsequently, the time series model SCINet is introduced for the first time into the field of bearing life prediction and used to predict the RUL of bearings. Finally, a series of multi-step prediction experiments are conducted on two publicly available datasets, PHM2012 and XJTU-SY, and compared with LSTM, GRU, Informer, and TCN models. The results show that the improved IF-SCINET has a stronger prediction capability compared to the traditional model, which significantly improves the accuracy and stability of bearing RUL prediction.

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Section: Introductionmentioning

confidence: 99%

Remaining Life Prediction of Bearings Based on Improved IF-SCINet

Zhang,

et al. 2024

IEEE Access

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Research on cutting tool edge geometry design based on SVR-PSO

Jiang,

Huang,

Tian

et al. 2024

Int J Adv Manuf Technol

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In order to optimize the design of tool edge, an intelligent method was used for modeling and optimization. The tool edge design method based on Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life, edge parameters, and processing condition parameters. Taking the tool life as the objective function, the optimal edge pro le design parameters were solved under different processing condition parameters. The T-shape tool validates was taken as a case for veri cation. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. This method was feasible and practical, and has been applied in the design department of tool manufacturing companies.

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Artificial intelligence-based approach to detect malicious users using deep learning and optimization techniques

Terumalasetti,

S R

2024

Multimed Tools Appl

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Research on Tool Remaining Life Prediction Method Based on CNN-LSTM-PSO

Cited by 9 publications

References 33 publications

Remaining Life Prediction of Bearings Based on Improved IF-SCINet

Remaining Life Prediction of Bearings Based on Improved IF-SCINet

Research on cutting tool edge geometry design based on SVR-PSO

Artificial intelligence-based approach to detect malicious users using deep learning and optimization techniques

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